US-12625086-B2 - Apparatus and process for medical imaging

US12625086B2US 12625086 B2US12625086 B2US 12625086B2US-12625086-B2

Abstract

A process for medical imaging, the process including: (i) receiving scattering data representing mono-static or multi-static measurements of scattering of electromagnetic signals from tissues of a body part of a subject at a plurality of different signal frequencies, wherein electromagnetic signals are emitted from one or more antennas and the corresponding scattered signals are measured by the one or more antennas; (ii) processing the scattering data to calculate electric field power values at each of a plurality of scattering locations of the subject's tissues within the body part and for each of the plurality of frequencies; (iii) for each of the scattering locations, summing the calculated electric field power values at the scattering location over the plurality of frequencies and the plurality of antennas to generate an image of the tissues within the body part; and (iv) iteratively updating a model of the tissues within the body part based on a comparison of the model with the generated image until a termination criterion is satisfied, wherein the updated model is output as an image of the subject's tissues within the body part.

Inventors

Amin ABBOSH
Arman AFASRI
Ali Zamani
Alina Bialkowski
Guohun ZHU
Thanh Phong NGUYEN
Lei Guo
Yifan Wang

Assignees

EMvision Medical Devices Ltd

Dates

Publication Date: 20260512
Application Date: 20190904
Priority Date: 20180904

Claims (19)

1 . A process for medical imaging, the process including: (i) receiving scattering data representing mono-static or multi-static measurements of scattering of electromagnetic signals from tissues of a body part of a subject at a plurality of different signal frequencies, wherein the electromagnetic signals are emitted from one or more antennas and the corresponding scattered electromagnetic signals are measured by the one or more antennas; (ii) using at least one processor, processing the scattering data to calculate electric field power values at each of a plurality of scattering locations of the subject's tissues within the body part and for each of the plurality of different signal frequencies; (iii) using the at least one processor, for each of the plurality of scattering locations, summing the calculated electric field power values at the respective scattering location over the plurality of different signal frequencies and the one or more antennas to generate an image of the tissues within the body part; and (iv) using the at least one processor, iteratively updating a model of the tissues within the body part based on a comparison of the model with the generated image until a termination criterion is satisfied, wherein the updated model is provided as an output image of the subject's tissues within the body part.
2 . The process of claim 1 , wherein the measurements are multi-static measurements and the one or more antennas comprise a plurality of antennas, wherein the electromagnetic signals are selectively emitted from each of the plurality of antennas disposed about the body part and the corresponding scattered electromagnetic signals are measured by each of the plurality of antennas.
3 . The process of claim 1 , wherein the body part is a head and the tissues include brain tissues of the subject.
4 . The process of claim 1 , including: (v) processing biodata of the subject using machine learning to select, from a library of templates, a base template as being the best match for the subject, wherein the templates represent respective models of example tissues of the body part of respective subjects, and the biodata of the subject represents at least age, gender, weight, and ethnicity of the subject; (vi) processing the selected base template and measurements of outer dimensions and/or shape of the subject's body part to generate template data representing the model of the tissues within the subject's body part by geometrically transforming spatial coordinates of the selected template to match the measurements of the subject's body part.
5 . The process of claim 1 , wherein the step of processing the scattering data includes the steps of: (v) normalising and removing clutter from the scattering data; and (vi) processing the normalised and clutter removed scattering data to calculate the electric field power values.
6 . The process of claim 5 , wherein removing clutter from the scattering data includes determining an average value of the measured scattered electromagnetic signals and subtracting the average value from each scattered electromagnetic signal measurement at each frequency to remove strong reflections and clutter from the scattering data.
7 . The process of claim 1 , including calibrating the scattering data by dividing measured scattering parameters for the body part by measured scattering parameters of an imaging domain in absence of the body part and with the imaging domain filled by a material with dielectric properties of a matching medium or an average body part phantom.
8 . The process of claim 1 , including classifying abnormal tissues within the body part as haemorrhagic or ischemic, by converting the scattering data from a frequency domain to a time-domain and mapping the time-domain scattering data to a corresponding graph, determining node degree and degree sequence properties of the graph, calculating graph degree mutual information to assess similarity of graphs, and training a classifier with a training set of graph degree mutual information features and their corresponding class labels and applying the classifier to the graphs calculated for the subject's tissues within the body part.
9 . The process of claim 1 , wherein the one or more antennas comprise a plurality of antennas and the process includes comparing the scattering data from corresponding pairs of opposed antennas among the plurality of antennas to identify significant differences between the scattering data for different hemispheres of the subject's brain, these being indicative of an abnormality in one of the hemispheres.
10 . A non-transitory computer-readable storage medium having stored thereon processor-executable instructions that, when executed by the at least one processor of a medical imaging system, cause the at least one processor to execute the process of claim 1 .
11 . An apparatus for medical imaging, including: (i) an input to receive scattering data representing mono-static or multi-static measurements of scattering of electromagnetic signals from tissues of a body part of a subject at a plurality of different signal frequencies, wherein the electromagnetic signals are emitted from one or more antennas and the corresponding scattered electromagnetic signals are measured by the one or more antennas; and (ii) a processor configured to: process the scattering data to calculate electric field power values at each of a plurality of scattering locations of the subject's tissues within the body part and for each of the plurality of different signal frequencies; for each of the plurality of scattering locations, sum the calculated electric field power values at the respective scattering location over the plurality of different signal frequencies and the one or more antennas to generate an image of the tissues within the body part; and iteratively update a model of the tissues within the body part based on a comparison of the model with the generated image until a termination criterion is satisfied, wherein the updated model is provided as an output image of the subject's tissues within the body part.
12 . The apparatus of claim 11 , wherein the measurements are multi-static measurements and the one or more antennas comprise a plurality of antennas, wherein the electromagnetic signals are selectively emitted from each of the plurality of antennas adapted to be disposed about the body part and the corresponding scattered electromagnetic signals are measured by each of the plurality of antennas.
13 . The apparatus of claim 11 , wherein the body part is a head and the tissues include brain tissues of the subject.
14 . The apparatus of claim 11 , including a template generator to: process biodata of the subject using machine learning to select, from a library of templates, a base template as being the best match for the subject, wherein the templates represent respective models of example tissues of the body part of respective subjects, and the biodata of the subject represents at least age, gender, weight, and ethnicity of the subject; and process the selected base template and measurements of outer dimensions and/or shape of the subject's body part to generate template data representing the model of the tissues within the subject's body part by geometrically transforming spatial coordinates of the selected template to match the measurements of the subject's body part.
15 . The apparatus of claim 11 , wherein the processor is configured to process the scattering data by: (v) normalising and removing clutter from the scattering data; and (vi) processing the normalised and clutter removed scattering data to calculate the electric field power values.
16 . The apparatus of claim 15 , wherein the processor is configured to remove clutter from the scattering data by determining an average value of the measured scattered electromagnetic signals and subtracting the average value from each scattered electromagnetic signal measurement at each frequency to remove strong reflections and clutter from the scattering data.
17 . The apparatus of claim 11 , wherein the processor is further configured to calibrate the scattering data by dividing measured scattering parameters for the body part by measured scattering parameters of an imaging domain in absence of the body part and with the imaging domain filled by a material with dielectric properties of a matching medium or an average body part phantom.
18 . The apparatus of claim 11 , wherein the processor is further configured to classify abnormal tissues within the body part as haemorrhagic or ischemic, by converting the scattering data from a frequency domain to a time-domain and mapping the time-domain scattering data to a corresponding graph, to determine node degree and degree sequence properties of the graph, to calculate graph degree mutual information to assess similarity of graphs, and to train a classifier with a training set of graph degree mutual information features and their corresponding class labels and apply the classifier to the graphs calculated for the subject's tissues within the body part.
19 . The apparatus of claim 11 , wherein the one or more antennas comprise a plurality of antennas and wherein the processor is further configured to compare the scattering data from corresponding pairs of opposed antennas among the plurality of antennas to identify significant differences between the scattering data for different hemispheres of the subject's brain, these being indicative of an abnormality in one of the hemispheres.

Description

This application is a U.S. national phase application under 35 U.S.C. § 371 of international patent application No. PCT/AU2019/050948, filed on Sep. 4, 2019, and entitled “APPARATUS AND PROCESS FOR MEDICAL IMAGING,” which claims priority to Australian patent application No. 2018903284, filed on Sep. 4, 2018, and to Australian patent application No. 2018903285, filed on Sep. 4, 2018, the entire contents of each of which are incorporated herein by reference. TECHNICAL FIELD The present invention relates to the field of medical imaging, in particular using signal processing and electromagnetic computational techniques to detect the presence and location of abnormalities inside tissues and to classify such abnormalities. BACKGROUND Medical imaging technologies such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI) and nuclear medicine imaging are extremely powerful techniques for imaging internal features of the human body, but suffer from a number of disadvantages that limit their applicability. For example, these technologies require expensive equipment, and are therefore not generally available at rural or remote health centres. Indeed, according to the World Health Organization (WHO), more than half of the world's population does not have access to diagnostic imaging. Furthermore, there is a general need for low-cost and safe imaging systems for the detection and continuous monitoring of a variety of diseases. Due to the need to limit exposure to ionising radiation such as X-rays, most currently available medical imaging systems cannot be used for frequent monitoring. Additionally, the bulky and static structures and high costs of MRI and other large medical imaging systems often preclude them for monitoring diseases that require activity monitoring on a regular and short-term basis, and is impractical for them to be used by paramedics for on the spot imaging and assessment purposes. Furthermore, conventional medical imaging tools are generally not suitable for urgent onsite diagnosis. For example, brain strokes are one of the main causes of disability and death worldwide. According to the Australian Stroke Foundation Organization, in 2017 55,831 Australians suffered a life-threatening stroke every nine minutes, and without taking an action this number will increase to one stroke every four minutes by 2050. Similarly, in case of brain injuries, rapid diagnosis is often essential to save the patient. Severe brain injuries include traumatic and acquired brain injuries, which are respectively caused by external forces (such as a fall or accident) or internal incidences (such as a stroke or tumor). It is well known that a patient with a brain injury requires immediate medical attention. From the onset of the brain injury, millions of brain cells die every second, causing permanent damage and in some cases death. Thus, a rapid and portable diagnosis system is required for rapid on the spot diagnosis of such injuries. Electromagnetic imaging is an attractive technique for medical applications, and has the potential to create a visual representation of the interior of the human body in a cost-effective and safe manner. From an electromagnetic engineering perspective, the human body is an electromagnetically heterogeneous medium characterized by features and tissues with different dielectric properties. Moreover, an injured tissue has different values of the dielectric properties permittivity and conductivity compared to healthy tissues. When an injured tissue with a high permittivity value compared to its neighbouring healthy tissue is exposed to an electromagnetic wave at a microwave frequency, a high portion of the wave is reflected back towards the radiation source. A microwave medical imaging system can be utilized to transmit electromagnetic waves into an object to be imaged, such as the human head. Microwave signals reflected by damaged tissues within the head (e.g., in particular at bleeding or clot sites within the brain) due to changes in electromagnetic properties are received and measured by the system, and data representing the measured signals can be processed to estimate the location and/or dielectric properties of the abnormality, and to generate two or three-dimensional images of the head showing the damaged tissues. The data processing step plays a critical role in an electromagnetic imaging system. Various imaging techniques have been employed to detect medical targets from measurements of scattered electromagnetic signals. Those techniques try to estimate the dielectric properties of the tissues by solving nonlinear equations (tomography), which do not have a unique solution, and that solution may not depend continuously on the input data, or to find the location of target using time-domain radar-based techniques. Due to the time-consuming nature of tomography-based techniques, they are almost exclusively applicable to single frequency or narrow-band multi-frequency signa